knitr::opts_chunk$set(echo = TRUE)
cmdstanr::set_cmdstan_path(path = "C:/Users/kueng/.cmdstan/cmdstan-2.35.0")
library(tidyverse)
library(R.utils)
library(wbCorr)
library(readxl)
library(kableExtra)
library(brms)
library(bayesplot)
library(beepr)
library(DHARMa)
source(file.path('Functions', 'ReportModels.R'))
source(file.path('Functions', 'PrettyTables.R'))
source(file.path('Functions', 'ReportMeasures.R'))
source(file.path('Functions', 'PrepareData.R'))## [1] 1116
# Set options for analysis
use_mi = FALSE
shutdown = TRUE
report_ordinal = FALSE
options(
dplyr.print_max = 100,
brms.backend = 'cmdstan',
brms.file_refit = ifelse(use_mi, 'never', 'on_change'),
#brms.file_refit = 'always',
error = function() beepr::beep(sound = 5)
)df <- openxlsx::read.xlsx('long.xlsx')
df_original <- df
df_list <- prepare_data(df, use_mi = use_mi)Constructing scales Re-coding pusing reshaping data (4field) centering data within and between
# Income (mean of both partner's report)
merge_income <- function(income1, income2) {
merged_income <- numeric(length(income1))
# Loop through each pair of incomes
for (i in seq_along(income1)) {
# Handle NA
if (is.na(income1[i])) {
merged_income[i] <- income2[i]
}
else if (is.na(income2[i])) {
merged_income[i] <- income1[i]
}
# if both are informative, take mean and round
else if (income1[i] %in% 1:6 && income2[i] %in% 1:6) {
merged_income[i] <- round((income1[i] + income2[i]) / 2)
}
# if one is informative and the other not, use the informative one.
else if (income1[i] %in% 1:6) {
merged_income[i] <- income1[i]
}
else if (income2[i] %in% 1:6) {
merged_income[i] <- income2[i]
}
# Now we only have cases, where both are either 70 or 99. We simply report "undisclosed".
else {
merged_income[i] <- 99
}
}
# Convert to factor
merged_income <- factor(
merged_income, levels = c(1,2,3,4,5,6,70,99),
labels = c(
"up to CHF 2'000.-",
"CHF 2'001.- to CHF 4'000.-",
"CHF 4'001.- to CHF 6'000.-",
"CHF 6'001.- to CHF 8'000.-",
"CHF 8'001.- to CHF 10'000.-",
"above CHF 10'000.-",
"I don't know",
"Undisclosed"
)
)
return(merged_income)
}
df_sample_report <- df_full %>%
group_by(coupleID) %>%
arrange(userID) %>%
# Computing couple level variables
mutate(
Household_Income = merge_income(first(pre_income_1), last(pre_income_1)),
reldur = pre_rel_duration_m / 12 + pre_rel_duration_y,
Relationship_duration = mean(reldur, na.rm = TRUE),
habdur = pre_hab_duration_m / 12 + pre_hab_duration_y,
Cohabiting_duration = mean(habdur, na.rm = TRUE),
Marital_status = factor(
case_when(
all(pre_mat_stat == 1) ~ "Married",
any(pre_mat_stat == 1) ~ "One Partner Married",
TRUE ~ "Not Married"
)
),
Have_children = factor(
(first(pre_child_option) + last(pre_child_option)) > 0,
levels = c(FALSE, TRUE),
labels = c('Have Children', 'No Children')),
Gender = factor(
gender,
levels = c(1,2,3),
labels = c('Male','Female', 'Other')),
Couple_type = as.factor(
case_when(
first(Gender) == last(Gender) & first(Gender) == 'Male' ~ 'Same-Sex Couple (Male)',
first(Gender) == last(Gender) & first(Gender) == 'Female' ~ 'Same-Sex Couple (Female)',
TRUE ~ 'Mixed-sex Couple'
)
)
) %>%
ungroup() %>%
# Individual level variables
mutate(
Age = pre_age,
Handedness = factor(
pre_handedness,
levels = c(0, 1, 2),
c('Right','Left', 'Ambidextrous')),
Highest_Education = factor(
pre_education,
levels = c(1,2,3,4,5,6,7),
labels = c(
"(still) no school diploma",
"compulsory education (9 years)",
"vocational training (apprenticeship)",
"Matura (university entrance qualification)",
"Bachelor's degree",
"Master's degree",
"Doctorate degree"
)
),
BMI = pre_weight / ((pre_height / 100)^2) # to meters
) %>%
select(c(Relationship_duration, Cohabiting_duration, Couple_type, Household_Income,
Marital_status, Have_children,
Gender, Age, Handedness, Highest_Education, BMI))
sample_table <- report_measures(df_sample_report, ICC = F)
sample_table$n_Obs <- as.numeric(sample_table$n_Obs) / 55
rownames(sample_table) <- NULL
n_couple_vars <- 17
sample_table$n_Obs[1:n_couple_vars] <- sample_table$n_Obs[1:n_couple_vars] / 2
packing_sample <- list(
"Couple level variables (38 couples)" =
c(1, n_couple_vars),
"Individual level variables (76 individuals)"
= c(n_couple_vars+1, nrow(sample_table))
)
df_sample_summary <- print_df(
sample_table,
rows_to_pack = packing_sample
)
export_xlsx(df_sample_summary,
file.path('Output', 'SampleDescription.xlsx'),
merge_option = 'both',
rows_to_pack = packing_sample,
colwidths = c(20,35,7,7,7,7,7,10)
)##
## Attaching package: 'rvest'
## The following object is masked from 'package:readr':
##
## guess_encoding
| Variable | Level | n_Obs | percentage_Obs | Missing | Mean | SD | Range |
|---|---|---|---|---|---|---|---|
| Couple level variables (38 couples) | |||||||
| Relationship_duration | NA | 38 | NA | 0% | 9.23 | 9.03 | 0.58-36.00 |
| Cohabiting_duration | NA | 38 | NA | 0% | 7.53 | 9.14 | 0.25-33.00 |
| Couple_type | Same-Sex Couple (Male) | 1 | 3% | NA | NA | NA | NA |
| Couple_type | Same-Sex Couple (Female) | 1 | 3% | NA | NA | NA | NA |
| Couple_type | Mixed-sex Couple | 36 | 95% | NA | NA | NA | NA |
| Household_Income | I don’t know | 0 | 0% | NA | NA | NA | NA |
| Household_Income | up to CHF 2’000.- | 2 | 5% | NA | NA | NA | NA |
| Household_Income | CHF 2’001.- to CHF 4’000.- | 3 | 8% | NA | NA | NA | NA |
| Household_Income | CHF 6’001.- to CHF 8’000.- | 3 | 8% | NA | NA | NA | NA |
| Household_Income | CHF 4’001.- to CHF 6’000.- | 5 | 13% | NA | NA | NA | NA |
| Household_Income | Undisclosed | 5 | 13% | NA | NA | NA | NA |
| Household_Income | CHF 8’001.- to CHF 10’000.- | 8 | 21% | NA | NA | NA | NA |
| Household_Income | above CHF 10’000.- | 12 | 32% | NA | NA | NA | NA |
| Marital_status | Married | 13 | 34% | NA | NA | NA | NA |
| Marital_status | Not Married | 25 | 66% | NA | NA | NA | NA |
| Have_children | No Children | 11 | 29% | NA | NA | NA | NA |
| Have_children | Have Children | 27 | 71% | NA | NA | NA | NA |
| Individual level variables (76 individuals) | |||||||
| Gender | Other | 0 | 0% | NA | NA | NA | NA |
| Gender | Male | 38 | 50% | NA | NA | NA | NA |
| Gender | Female | 38 | 50% | NA | NA | NA | NA |
| Age | NA | 76 | NA | 0% | 34.01 | 10.96 | 19.00-60.00 |
| Handedness | Ambidextrous | 1 | 1% | NA | NA | NA | NA |
| Handedness | Left | 11 | 14% | NA | NA | NA | NA |
| Handedness | Right | 64 | 84% | NA | NA | NA | NA |
| Highest_Education | (still) no school diploma | 0 | 0% | NA | NA | NA | NA |
| Highest_Education | compulsory education (9 years) | 0 | 0% | NA | NA | NA | NA |
| Highest_Education | Doctorate degree | 0 | 0% | NA | NA | NA | NA |
| Highest_Education | vocational training (apprenticeship) | 8 | 11% | NA | NA | NA | NA |
| Highest_Education | Master’s degree | 21 | 28% | NA | NA | NA | NA |
| Highest_Education | Bachelor’s degree | 23 | 30% | NA | NA | NA | NA |
| Highest_Education | Matura (university entrance qualification) | 24 | 32% | NA | NA | NA | NA |
| BMI | NA | 76 | NA | 0% | 24.94 | 4.08 | 16.37-33.95 |
main_constructs <- c("persuasion", "pressure","pushing",
"pa_sub", "pa_obj", "aff", "reactance"
)
main_descriptives <- report_measures(
data = df_full,
measures = main_constructs,
ICC = TRUE,
cluster_var = df_full$userID)
openxlsx::write.xlsx(
main_descriptives,
file.path('Output', 'DescriptivesMain.xlsx')
)
print_df(main_descriptives)| Variable | n_Obs | Missing | Mean | SD | Range | ICC |
|---|---|---|---|---|---|---|
| persuasion | 4180 | 6% | 0.42 | 1.08 | 0.00- 5.00 | 0.23 |
| pressure | 4180 | 6% | 0.12 | 0.62 | 0.00- 5.00 | 0.58 |
| pushing | 4180 | 6% | 0.16 | 0.66 | 0.00- 5.00 | 0.11 |
| pa_sub | 4180 | 6% | 30.40 | 54.78 | 0.00-720.00 | 0.16 |
| pa_obj | 4180 | 11% | 144.41 | 117.81 | 5.75-971.25 | 0.18 |
| aff | 4180 | 6% | 4.83 | 1.14 | 1.00- 6.00 | 0.41 |
| reactance | 4180 | 81% | 0.79 | 1.32 | 0.00- 5.00 | 0.47 |
all_constructs <- c(
main_constructs,
"day",
"weartime",
"isWeekend",
"plan",
"studyGroup",
"support",
"got_JITAI",
"skilled_support"
)
all_descriptives <- report_measures(df_full, all_constructs, ICC = F)
openxlsx::write.xlsx(
all_descriptives,
file.path('Output', 'DescriptivesAll.xlsx')
)
print_df(all_descriptives)| Variable | Level | n_Obs | percentage_Obs | Missing | Mean | SD | Range |
|---|---|---|---|---|---|---|---|
| persuasion | NA | 4180 | NA | 6% | 0.42 | 1.08 | 0.00- 5.00 |
| pressure | NA | 4180 | NA | 6% | 0.12 | 0.62 | 0.00- 5.00 |
| pushing | NA | 4180 | NA | 6% | 0.16 | 0.66 | 0.00- 5.00 |
| pa_sub | NA | 4180 | NA | 6% | 30.40 | 54.78 | 0.00- 720.00 |
| pa_obj | NA | 4180 | NA | 11% | 144.41 | 117.81 | 5.75- 971.25 |
| aff | NA | 4180 | NA | 6% | 4.83 | 1.14 | 1.00- 6.00 |
| reactance | NA | 4180 | NA | 81% | 0.79 | 1.32 | 0.00- 5.00 |
| day | NA | 4180 | NA | 0% | 0.50 | 0.29 | 0.00- 1.00 |
| weartime | NA | 4180 | NA | 1% | 1057.42 | 384.29 | 0.00-1440.00 |
| isWeekend | Weekend | 1216 | 29% | NA | NA | NA | NA |
| isWeekend | Weekday | 2964 | 71% | NA | NA | NA | NA |
| plan | missing | 238 | 6% | NA | NA | NA | NA |
| plan | Plan | 1860 | 44% | NA | NA | NA | NA |
| plan | No plan | 2082 | 50% | NA | NA | NA | NA |
| studyGroup | last 3 weeks interventions | 1320 | 32% | NA | NA | NA | NA |
| studyGroup | Allways inerventions | 1430 | 34% | NA | NA | NA | NA |
| studyGroup | First 3 weeks interventions | 1430 | 34% | NA | NA | NA | NA |
| support | NA | 4180 | NA | 6% | 0.91 | 1.52 | 0.00- 5.00 |
| got_JITAI | JITAI received | 585 | 14% | NA | NA | NA | NA |
| got_JITAI | No JITAI | 3595 | 86% | NA | NA | NA | NA |
| skilled_support | Days before Intervention | 1036 | 25% | NA | NA | NA | NA |
| skilled_support | Days after Intervention | 3144 | 75% | NA | NA | NA | NA |
cors <- wbCorr(df_full[,c(main_constructs)], df_full$coupleID, method = 'spearman')
main_cors <- summary(cors, 'wb')$merged_wb
openxlsx::write.xlsx(
main_cors,
file.path('Output', 'Correlations.xlsx')
)
print_df(main_cors, width = '7em')| persuasion | pressure | pushing | pa_sub | pa_obj | aff | reactance | |
|---|---|---|---|---|---|---|---|
| persuasion | [0.20] | 0.21*** | 0.40*** | 0.17*** | 0.07*** | 0.00 | -0.05 |
| pressure | 0.30 | [0.55] | 0.28*** | -0.03 | -0.04* | -0.01 | 0.26*** |
| pushing | 0.63*** | 0.40* | [0.08] | 0.14*** | 0.05** | 0.01 | 0.07* |
| pa_sub | 0.27 | -0.10 | 0.24 | [0.15] | 0.31*** | 0.20*** | -0.04 |
| pa_obj | 0.13 | -0.08 | 0.27 | 0.51*** | [0.13] | 0.19*** | 0.06 |
| aff | 0.28 | -0.07 | 0.29 | 0.52*** | 0.22 | [0.28] | -0.01 |
| reactance | 0.18 | 0.23 | 0.11 | 0.06 | 0.38* | -0.12 | [0.42] |
Within-person correlations are above the diagonal and between-person
correlations are below the diagonal.
On the diagonal are intraclass correlations (ICCs)
# For indistinguishable Dyads
model_rows_fixed <- c(
'Intercept',
# '-- WITHIN PERSON MAIN EFFECTS --',
'persuasion_self_cw',
'persuasion_partner_cw',
'pressure_self_cw',
'pressure_partner_cw',
'pushing_self_cw',
'pushing_partner_cw',
'day',
'weartime_self_cw',
# '-- BETWEEN PERSON MAIN EFFECTS',
'persuasion_self_cb',
'persuasion_partner_cb',
'pressure_self_cb',
'pressure_partner_cb',
'pushing_self_cb',
'pushing_partner_cb',
'weartime_self_cb'
)
model_rows_fixed_ordinal <- c(
model_rows_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rows_fixed[2:length(model_rows_fixed)]
)
model_rows_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(persuasion_self_cw)',
'sd(persuasion_partner_cw)',
'sd(pressure_self_cw)',
'sd(pressure_partner_cw)',
'sd(pushing_self_cw)',
'sd(pushing_partner_cw)',
# '-- CORRELATION STRUCTURE -- ',
'ar[1]',
'nu',
'shape',
'sderr',
'sigma'
)
model_rows_random_ordinal <- c(model_rows_random,'disc')# For indistinguishable Dyads
model_rownames_fixed <- c(
'Intercept',
# '-- WITHIN PERSON MAIN EFFECTS --',
'Daily received persuasion target -> target',
'Daily received persuasion target -> agent',
'Daily received pressure target -> target',
'Daily received pressure target -> agent',
'Daily received pushing target -> target',
'Daily received pushing target -> agent',
'Day',
'Daily weartime',
# '-- BETWEEN PERSON MAIN EFFECTS',
'Mean received persuasion target -> target',
'Mean received persuasion target -> agent',
'Mean received pressure target -> target',
'Mean received pressure target -> agent',
'Mean received pushing target -> target',
'Mean received pushing target -> agent',
'Mean weartime'
)
model_rownames_fixed_ordinal <- c(
model_rownames_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rownames_fixed[2:length(model_rownames_fixed)]
)
model_rownames_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(Daily received persuasion target -> target)',
'sd(Daily received persuasion target -> agent)',
'sd(Daily received pressure target -> target)',
'sd(Daily received pressure target -> agent)',
'sd(Daily received pushing target -> target)',
'sd(Daily received pushing target -> agent)',
# '-- CORRELATION STRUCTURE -- ',
'ar[1]',
'nu',
'shape',
'sderr',
'sigma'
)
model_rownames_random_ordinal <- c(model_rownames_random,'disc')# For indistinguishable Dyads
model_rownames_fixed <- c(
"Intercept",
# "-- WITHIN PERSON MAIN EFFECTS --",
"Daily persuasion experienced",
"Daily persuasion utilized (partner's view)", # OR partner received
"Daily pressure experienced",
"Daily pressure utilized (partner's view)",
"Daily pushing experienced",
"Daily pushing utilized (partner's view)",
"Day",
"Daily weartime",
# "-- BETWEEN PERSON MAIN EFFECTS",
"Mean persuasion experienced",
"Mean persuasion utilized (partner's view)",
"Mean pressure experienced",
"Mean pressure utilized (partner's view)",
"Mean pushing experienced",
"Mean pushing utilized (partner's view)",
"Mean weartime"
)
model_rownames_fixed_ordinal <- c(
model_rownames_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rownames_fixed[2:length(model_rownames_fixed)]
)
model_rownames_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
"sd(Daily persuasion experienced)",
"sd(Daily persuasion utilized (partner's view))", # OR partner received
"sd(Daily pressure experienced)",
"sd(Daily pressure utilized (partner's view))",
"sd(Daily pushing experienced)",
"sd(Daily pushing utilized (partner's view))",
# '-- CORRELATION STRUCTURE -- ',
'ar[1]',
'nu',
'shape',
'sderr',
'sigma'
)
model_rownames_random_ordinal <- c(model_rownames_random,'disc')rows_to_pack <- list(
"Within-Person Effects" = c(2,9),
"Between-Person Effects" = c(10,16),
"Random Effects" = c(17, 23),
"Additional Parameters" = c(24,28)
)
rows_to_pack_ordinal <- list(
"Intercepts" = c(1,6),
"Within-Person Effects" = c(2+5,9+5),
"Between-Person Effects" = c(10+5,16+5),
"Random Effects" = c(17+5, 23+5),
"Additional Parameters" = c(24+5,28+6)
)## [1] 0 720
Modelling using the gaussian family fails. Due to the many zeros, transformations won’t help estimating the models. We employ the negative binomial family.
formula <- bf(
pa_sub ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b"),
brms::set_prior("normal(0, 50)", class = "Intercept", lb = 0),
brms::set_prior("normal(0, 10)", class = "sd", group = "coupleID", lb = 0),
brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
brms::set_prior("cauchy(0, 20)", class = "shape"),
brms::set_prior("cauchy(0, 10)", class='sderr')
)
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
pa_sub <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::negbinomial(),
#control = list(adapt_delta = 0.99),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "pa_sub")
)## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
##
## Computed from 12000 by 3736 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -12060.6 177.6
## p_loo 30.2 2.8
## looic 24121.2 355.2
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.3, 1.6]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 3732 99.9% 662
## (0.7, 1] (bad) 3 0.1% <NA>
## (1, Inf) (very bad) 1 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
summarize_brms(
pa_sub,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack)| IRR | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| Intercept | 27.35* | 21.03 | 35.95 | 1.001 | 3902.26 | 6229.26 |
| Within-Person Effects | ||||||
| Daily persuasion experienced | 1.20* | 1.07 | 1.35 | 1.000 | 10192.93 | 7278.69 |
| Daily persuasion utilized (partner’s view) | 1.19* | 1.06 | 1.34 | 1.000 | 12159.42 | 8804.61 |
| Daily pressure experienced | 0.94 | 0.71 | 1.30 | 1.000 | 9685.08 | 7640.07 |
| Daily pressure utilized (partner’s view) | 1.16 | 0.88 | 1.59 | 1.000 | 11249.31 | 7539.42 |
| Daily pushing experienced | 1.13 | 0.92 | 1.41 | 1.000 | 8816.39 | 8383.16 |
| Daily pushing utilized (partner’s view) | 1.13 | 0.95 | 1.36 | 1.000 | 12547.79 | 9213.16 |
| Day | 0.79 | 0.58 | 1.10 | 1.000 | 12431.32 | 9455.82 |
| Daily weartime | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | ||||||
| Mean persuasion experienced | 1.61 | 0.83 | 3.13 | 1.002 | 3172.48 | 5991.41 |
| Mean persuasion utilized (partner’s view) | 1.19 | 0.62 | 2.28 | 1.002 | 3128.46 | 5435.36 |
| Mean pressure experienced | 0.50 | 0.22 | 1.11 | 1.001 | 4626.65 | 6921.35 |
| Mean pressure utilized (partner’s view) | 0.43* | 0.19 | 0.96 | 1.003 | 4346.50 | 7133.59 |
| Mean pushing experienced | 1.71 | 0.64 | 4.56 | 1.000 | 4336.92 | 5727.04 |
| Mean pushing utilized (partner’s view) | 2.03 | 0.76 | 5.67 | 1.001 | 4271.64 | 6953.82 |
| Mean weartime | NA | NA | NA | NA | NA | NA |
| Random Effects | ||||||
| sd(Intercept) | 0.61 | 0.45 | 0.83 | 1.00 | 3236.86 | 5929.19 |
| sd(Daily persuasion experienced) | 0.09 | 0.00 | 0.24 | 1.00 | 4277.64 | 4971.03 |
| sd(Daily persuasion utilized (partner’s view)) | 0.08 | 0.00 | 0.21 | 1.00 | 5008.36 | 5206.23 |
| sd(Daily pressure experienced) | 0.17 | 0.01 | 0.53 | 1.00 | 6480.10 | 5580.93 |
| sd(Daily pressure utilized (partner’s view)) | 0.16 | 0.01 | 0.49 | 1.00 | 7007.22 | 4748.59 |
| sd(Daily pushing experienced) | 0.28 | 0.02 | 0.58 | 1.00 | 3266.42 | 3678.71 |
| sd(Daily pushing utilized (partner’s view)) | 0.12 | 0.00 | 0.32 | 1.00 | 4609.50 | 3727.29 |
| Additional Parameters | ||||||
| ar[1] | 0.03 | -0.94 | 0.94 | 1.00 | 10005.25 | 6639.54 |
| nu | NA | NA | NA | NA | NA | NA |
| shape | 0.14 | 0.13 | 0.14 | 1.00 | 13938.19 | 8436.71 |
| sderr | 0.05 | 0.00 | 0.13 | 1.00 | 6131.05 | 5072.35 |
| sigma | NA | NA | NA | NA | NA | NA |
Family: negbinomial Links: mu = log; shape = identity Formula: pa_sub ~ persuasion_self_cw + persuasion_partner_cw + pressure_self_cw + pressure_partner_cw + pushing_self_cw + pushing_partner_cw + persuasion_self_cb + persuasion_partner_cb + pressure_self_cb + pressure_partner_cb + pushing_self_cb + pushing_partner_cb + day + (persuasion_self_cw + persuasion_partner_cw + pressure_self_cw + pressure_partner_cw + pushing_self_cw + pushing_partner_cw | coupleID) autocor ~ ar(time = day, gr = coupleID:userID, p = 1) Data: data (Number of observations: 3736) Draws: 4 chains, each with iter = 5000; warmup = 2000; thin = 1; total post-warmup draws = 12000
Correlation Structures: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS ar[1] 0.03 0.55 -0.94 0.94 1.00 10005 6640 sderr 0.05 0.04 0.00 0.13 1.00 6131 5072
Multilevel Hyperparameters: ~coupleID (Number of levels: 38) Estimate Est.Error l-95% CI sd(Intercept) 0.61 0.10 0.45 sd(persuasion_self_cw) 0.09 0.06 0.00 sd(persuasion_partner_cw) 0.08 0.06 0.00 sd(pressure_self_cw) 0.17 0.14 0.01 sd(pressure_partner_cw) 0.16 0.13 0.01 sd(pushing_self_cw) 0.28 0.15 0.02 sd(pushing_partner_cw) 0.12 0.08 0.00 cor(Intercept,persuasion_self_cw) -0.19 0.33 -0.74 cor(Intercept,persuasion_partner_cw) -0.19 0.34 -0.75 cor(persuasion_self_cw,persuasion_partner_cw) 0.10 0.35 -0.60 cor(Intercept,pressure_self_cw) -0.05 0.34 -0.67 cor(persuasion_self_cw,pressure_self_cw) -0.00 0.35 -0.67 cor(persuasion_partner_cw,pressure_self_cw) 0.01 0.36 -0.67 cor(Intercept,pressure_partner_cw) -0.08 0.35 -0.71 cor(persuasion_self_cw,pressure_partner_cw) 0.02 0.35 -0.65 cor(persuasion_partner_cw,pressure_partner_cw) 0.02 0.35 -0.65 cor(pressure_self_cw,pressure_partner_cw) 0.02 0.35 -0.66 cor(Intercept,pushing_self_cw) -0.29 0.32 -0.81 cor(persuasion_self_cw,pushing_self_cw) 0.02 0.35 -0.65 cor(persuasion_partner_cw,pushing_self_cw) -0.00 0.35 -0.66 cor(pressure_self_cw,pushing_self_cw) -0.02 0.34 -0.67 cor(pressure_partner_cw,pushing_self_cw) -0.02 0.35 -0.67 cor(Intercept,pushing_partner_cw) -0.19 0.35 -0.77 cor(persuasion_self_cw,pushing_partner_cw) 0.04 0.36 -0.64 cor(persuasion_partner_cw,pushing_partner_cw) -0.01 0.35 -0.67 cor(pressure_self_cw,pushing_partner_cw) 0.01 0.35 -0.67 cor(pressure_partner_cw,pushing_partner_cw) -0.03 0.35 -0.69 cor(pushing_self_cw,pushing_partner_cw) 0.18 0.34 -0.53 u-95% CI Rhat Bulk_ESS Tail_ESS sd(Intercept) 0.83 1.00 3237 5929 sd(persuasion_self_cw) 0.24 1.00 4278 4971 sd(persuasion_partner_cw) 0.21 1.00 5008 5206 sd(pressure_self_cw) 0.53 1.00 6480 5581 sd(pressure_partner_cw) 0.49 1.00 7007 4749 sd(pushing_self_cw) 0.58 1.00 3266 3679 sd(pushing_partner_cw) 0.32 1.00 4610 3727 cor(Intercept,persuasion_self_cw) 0.51 1.00 8924 7912 cor(Intercept,persuasion_partner_cw) 0.52 1.00 11582 9686 cor(persuasion_self_cw,persuasion_partner_cw) 0.73 1.00 9260 8874 cor(Intercept,pressure_self_cw) 0.60 1.00 12578 8674 cor(persuasion_self_cw,pressure_self_cw) 0.65 1.00 10920 8869 cor(persuasion_partner_cw,pressure_self_cw) 0.67 1.00 10471 9104 cor(Intercept,pressure_partner_cw) 0.61 1.00 11919 8830 cor(persuasion_self_cw,pressure_partner_cw) 0.68 1.00 12157 8725 cor(persuasion_partner_cw,pressure_partner_cw) 0.68 1.00 10710 9131 cor(pressure_self_cw,pressure_partner_cw) 0.67 1.00 10206 9279 cor(Intercept,pushing_self_cw) 0.40 1.00 6400 8194 cor(persuasion_self_cw,pushing_self_cw) 0.67 1.00 6734 8475 cor(persuasion_partner_cw,pushing_self_cw) 0.66 1.00 7619 9037 cor(pressure_self_cw,pushing_self_cw) 0.64 1.00 7289 8797 cor(pressure_partner_cw,pushing_self_cw) 0.64 1.00 7926 9447 cor(Intercept,pushing_partner_cw) 0.52 1.00 10647 8469 cor(persuasion_self_cw,pushing_partner_cw) 0.69 1.00 10264 9000 cor(persuasion_partner_cw,pushing_partner_cw) 0.66 1.00 10524 9148 cor(pressure_self_cw,pushing_partner_cw) 0.67 1.00 9644 8726 cor(pressure_partner_cw,pushing_partner_cw) 0.64 1.00 8310 9687 cor(pushing_self_cw,pushing_partner_cw) 0.76 1.00 7136 9667
Regression Coefficients: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Intercept 3.31 0.14 3.05 3.58 1.00 3902 persuasion_self_cw 0.18 0.06 0.07 0.30 1.00 10193 persuasion_partner_cw 0.17 0.06 0.06 0.29 1.00 12159 pressure_self_cw -0.06 0.15 -0.35 0.26 1.00 9685 pressure_partner_cw 0.15 0.15 -0.13 0.46 1.00 11249 pushing_self_cw 0.12 0.11 -0.09 0.34 1.00 8816 pushing_partner_cw 0.13 0.09 -0.05 0.31 1.00 12548 persuasion_self_cb 0.48 0.34 -0.18 1.14 1.00 3172 persuasion_partner_cb 0.17 0.34 -0.48 0.83 1.00 3128 pressure_self_cb -0.69 0.40 -1.50 0.11 1.00 4627 pressure_partner_cb -0.86 0.41 -1.67 -0.04 1.00 4346 pushing_self_cb 0.54 0.50 -0.45 1.52 1.00 4337 pushing_partner_cb 0.71 0.51 -0.27 1.73 1.00 4272 day -0.23 0.16 -0.55 0.09 1.00 12431 Tail_ESS Intercept 6229 persuasion_self_cw 7279 persuasion_partner_cw 8805 pressure_self_cw 7640 pressure_partner_cw 7539 pushing_self_cw 8383 pushing_partner_cw 9213 persuasion_self_cb 5991 persuasion_partner_cb 5435 pressure_self_cb 6921 pressure_partner_cb 7134 pushing_self_cb 5727 pushing_partner_cb 6954 day 9456
Further Distributional Parameters: Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS shape 0.14 0.00 0.13 0.14 1.00 13938 8437
Draws were sampled using sample(hmc). For each parameter, Bulk_ESS and Tail_ESS are effective sample size measures, and Rhat is the potential scale reduction factor on split chains (at convergence, Rhat = 1).
## [1] 5.75 971.25
We tried negative binomial here as well for consistency, but the model did not converge. Poisson also did not work. As we have no zeros in this distribution, we log transform.
formula <- bf(
pa_obj_log ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day + weartime_self_cw + weartime_self_cb +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b"),
brms::set_prior("normal(0, 50)", class = "Intercept", lb = 0),
brms::set_prior("normal(0, 10)", class = "sd", group = "coupleID", lb = 0),
brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
pa_obj_log <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.99),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "pa_obj_log")
)## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
##
## Computed from 12000 by 3337 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -2813.7 56.5
## p_loo 91.6 4.5
## looic 5627.5 113.0
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 1.9]).
##
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
summarize_brms(
pa_obj_log,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack)| exp(Est.) | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| Intercept | 118.09* | 106.05 | 131.83 | 1.002 | 4338.46 | 6795.25 |
| Within-Person Effects | ||||||
| Daily persuasion experienced | 1.03 | 1.00 | 1.06 | 1.001 | 10995.72 | 9537.78 |
| Daily persuasion utilized (partner’s view) | 1.02 | 0.99 | 1.05 | 1.000 | 12883.20 | 8691.54 |
| Daily pressure experienced | 0.95 | 0.89 | 1.01 | 1.001 | 17830.48 | 9885.92 |
| Daily pressure utilized (partner’s view) | 0.98 | 0.92 | 1.05 | 1.000 | 19278.32 | 10246.31 |
| Daily pushing experienced | 1.03 | 0.98 | 1.07 | 1.000 | 14025.59 | 9055.59 |
| Daily pushing utilized (partner’s view) | 1.03 | 0.99 | 1.07 | 1.000 | 21949.21 | 9623.34 |
| Day | 0.96 | 0.88 | 1.05 | 1.000 | 31435.94 | 8368.60 |
| Daily weartime | 1.00* | 1.00 | 1.00 | 1.000 | 11933.20 | 7885.13 |
| Between-Person Effects | ||||||
| Mean persuasion experienced | 1.09 | 0.81 | 1.45 | 1.001 | 3608.13 | 6059.46 |
| Mean persuasion utilized (partner’s view) | 0.96 | 0.72 | 1.29 | 1.001 | 3610.97 | 5890.12 |
| Mean pressure experienced | 0.97 | 0.71 | 1.34 | 1.001 | 5511.94 | 7801.62 |
| Mean pressure utilized (partner’s view) | 0.98 | 0.72 | 1.32 | 1.001 | 4870.74 | 7220.18 |
| Mean pushing experienced | 1.02 | 0.66 | 1.56 | 1.000 | 5260.03 | 7662.48 |
| Mean pushing utilized (partner’s view) | 1.29 | 0.85 | 1.96 | 1.000 | 5271.31 | 7350.00 |
| Mean weartime | 1.00 | 1.00 | 1.00 | 1.000 | 17022.21 | 9961.31 |
| Random Effects | ||||||
| sd(Intercept) | 0.30 | 0.23 | 0.39 | 1.00 | 4214.82 | 7324.07 |
| sd(Daily persuasion experienced) | 0.05 | 0.03 | 0.08 | 1.00 | 7644.01 | 6901.55 |
| sd(Daily persuasion utilized (partner’s view)) | 0.05 | 0.02 | 0.08 | 1.00 | 5351.19 | 3858.94 |
| sd(Daily pressure experienced) | 0.05 | 0.00 | 0.14 | 1.00 | 6659.89 | 6610.33 |
| sd(Daily pressure utilized (partner’s view)) | 0.04 | 0.00 | 0.11 | 1.00 | 7701.49 | 6409.62 |
| sd(Daily pushing experienced) | 0.06 | 0.00 | 0.14 | 1.00 | 3294.00 | 5715.05 |
| sd(Daily pushing utilized (partner’s view)) | 0.03 | 0.00 | 0.08 | 1.00 | 6464.76 | 7907.69 |
| Additional Parameters | ||||||
| ar[1] | 0.29 | 0.26 | 0.33 | 1.00 | 25732.94 | 7825.38 |
| nu | NA | NA | NA | NA | NA | NA |
| shape | NA | NA | NA | NA | NA | NA |
| sderr | NA | NA | NA | NA | NA | NA |
| sigma | 0.55 | 0.54 | 0.57 | 1.00 | 22829.74 | 8539.10 |
## [1] 1 6
formula <- bf(
aff ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
brms::set_prior("normal(0, 5)", class = "b"),
brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=6), # range of the outcome scale
brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),
brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
mood <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "mood")
)## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
##
## Computed from 12000 by 3736 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -12060.6 177.6
## p_loo 30.2 2.8
## looic 24121.2 355.2
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.3, 1.6]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 3732 99.9% 662
## (0.7, 1] (bad) 3 0.1% <NA>
## (1, Inf) (very bad) 1 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
summarize_brms(
mood,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = F) %>%
print_df(rows_to_pack = rows_to_pack)| b | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| Intercept | 4.73* | 4.52 | 4.94 | 1.001 | 3046.66 | 4422.02 |
| Within-Person Effects | ||||||
| Daily persuasion experienced | 0.00 | -0.03 | 0.04 | 1.000 | 17297.90 | 9842.88 |
| Daily persuasion utilized (partner’s view) | 0.02 | -0.02 | 0.05 | 1.001 | 14451.84 | 9886.46 |
| Daily pressure experienced | -0.05 | -0.16 | 0.05 | 1.000 | 12202.80 | 8571.79 |
| Daily pressure utilized (partner’s view) | -0.04 | -0.18 | 0.09 | 1.001 | 11104.70 | 8944.62 |
| Daily pushing experienced | 0.02 | -0.04 | 0.09 | 1.000 | 12680.12 | 9243.11 |
| Daily pushing utilized (partner’s view) | 0.06* | 0.00 | 0.12 | 1.000 | 13534.43 | 9546.16 |
| Day | 0.22* | 0.06 | 0.38 | 1.000 | 24416.16 | 8358.49 |
| Daily weartime | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | ||||||
| Mean persuasion experienced | 0.32 | -0.26 | 0.88 | 1.001 | 2292.03 | 3556.47 |
| Mean persuasion utilized (partner’s view) | 0.23 | -0.36 | 0.80 | 1.001 | 2249.94 | 3776.58 |
| Mean pressure experienced | -0.29 | -0.89 | 0.30 | 1.001 | 2937.39 | 4951.55 |
| Mean pressure utilized (partner’s view) | -0.31 | -0.89 | 0.28 | 1.001 | 2908.62 | 4194.29 |
| Mean pushing experienced | 0.25 | -0.55 | 1.04 | 1.000 | 3993.12 | 6072.68 |
| Mean pushing utilized (partner’s view) | 0.39 | -0.41 | 1.18 | 1.001 | 3968.95 | 6710.30 |
| Mean weartime | NA | NA | NA | NA | NA | NA |
| Random Effects | ||||||
| sd(Intercept) | 0.59 | 0.46 | 0.77 | 1.00 | 3439.67 | 6053.64 |
| sd(Daily persuasion experienced) | 0.03 | 0.00 | 0.07 | 1.00 | 5380.88 | 6051.41 |
| sd(Daily persuasion utilized (partner’s view)) | 0.06 | 0.01 | 0.11 | 1.00 | 3288.91 | 3865.59 |
| sd(Daily pressure experienced) | 0.11 | 0.01 | 0.28 | 1.00 | 4275.51 | 5244.59 |
| sd(Daily pressure utilized (partner’s view)) | 0.19 | 0.03 | 0.37 | 1.00 | 4126.88 | 4360.12 |
| sd(Daily pushing experienced) | 0.09 | 0.02 | 0.17 | 1.00 | 4973.05 | 3524.43 |
| sd(Daily pushing utilized (partner’s view)) | 0.05 | 0.00 | 0.13 | 1.00 | 5294.70 | 5550.35 |
| Additional Parameters | ||||||
| ar[1] | 0.45 | 0.42 | 0.48 | 1.00 | 20444.09 | 8148.47 |
| nu | NA | NA | NA | NA | NA | NA |
| shape | NA | NA | NA | NA | NA | NA |
| sderr | NA | NA | NA | NA | NA | NA |
| sigma | 0.87 | 0.85 | 0.89 | 1.00 | 21206.93 | 8694.13 |
## [1] 0 5
formula <- bf(
reactance ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
brms::set_prior("normal(0, 5)", class = "b"),
brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=5), # range of the outcome scale
brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),
brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
reactance <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "reactance")
)## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
##
## Computed from 12000 by 756 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -1059.5 35.7
## p_loo 75.2 7.5
## looic 2118.9 71.3
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 1.8]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 747 98.8% 174
## (0.7, 1] (bad) 9 1.2% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
summarize_brms(
reactance,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = F) %>%
print_df(rows_to_pack = rows_to_pack)| b | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| Intercept | 0.51* | 0.33 | 0.70 | 1.000 | 16417.76 | 9892.94 |
| Within-Person Effects | ||||||
| Daily persuasion experienced | -0.05 | -0.11 | 0.01 | 1.000 | 19356.46 | 9444.65 |
| Daily persuasion utilized (partner’s view) | 0.00 | -0.06 | 0.07 | 1.000 | 17176.01 | 9831.82 |
| Daily pressure experienced | 0.26* | 0.04 | 0.47 | 1.000 | 11824.37 | 9336.31 |
| Daily pressure utilized (partner’s view) | 0.14 | -0.06 | 0.40 | 1.000 | 9892.43 | 7761.10 |
| Daily pushing experienced | 0.08* | 0.00 | 0.17 | 1.000 | 12338.51 | 9197.36 |
| Daily pushing utilized (partner’s view) | -0.02 | -0.10 | 0.07 | 1.000 | 19544.32 | 8481.36 |
| Day | 0.10 | -0.17 | 0.35 | 1.000 | 24220.25 | 9140.35 |
| Daily weartime | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | ||||||
| Mean persuasion experienced | 0.08 | -0.29 | 0.45 | 1.000 | 10206.81 | 9393.41 |
| Mean persuasion utilized (partner’s view) | 0.14 | -0.25 | 0.55 | 1.000 | 10672.14 | 9527.96 |
| Mean pressure experienced | 0.62* | 0.21 | 1.03 | 1.000 | 12564.34 | 9244.78 |
| Mean pressure utilized (partner’s view) | 0.16 | -0.28 | 0.58 | 1.000 | 12489.27 | 9110.28 |
| Mean pushing experienced | -0.28 | -0.82 | 0.27 | 1.000 | 12347.15 | 10160.83 |
| Mean pushing utilized (partner’s view) | -0.59* | -1.16 | -0.01 | 1.000 | 13098.23 | 9577.68 |
| Mean weartime | NA | NA | NA | NA | NA | NA |
| Random Effects | ||||||
| sd(Intercept) | 0.27 | 0.15 | 0.41 | 1.00 | 6292.46 | 7778.91 |
| sd(Daily persuasion experienced) | 0.05 | 0.00 | 0.13 | 1.00 | 3879.53 | 6215.13 |
| sd(Daily persuasion utilized (partner’s view)) | 0.04 | 0.00 | 0.13 | 1.00 | 7142.65 | 6976.51 |
| sd(Daily pressure experienced) | 0.39 | 0.22 | 0.62 | 1.00 | 6664.96 | 9032.62 |
| sd(Daily pressure utilized (partner’s view)) | 0.25 | 0.01 | 0.63 | 1.00 | 3287.32 | 6408.13 |
| sd(Daily pushing experienced) | 0.10 | 0.01 | 0.23 | 1.00 | 4010.11 | 5667.82 |
| sd(Daily pushing utilized (partner’s view)) | 0.05 | 0.00 | 0.15 | 1.00 | 8780.84 | 7897.48 |
| Additional Parameters | ||||||
| ar[1] | 0.01 | -0.08 | 0.09 | 1.00 | 20581.26 | 9269.53 |
| nu | NA | NA | NA | NA | NA | NA |
| shape | NA | NA | NA | NA | NA | NA |
| sderr | NA | NA | NA | NA | NA | NA |
| sigma | 0.93 | 0.88 | 0.98 | 1.00 | 14809.72 | 8576.95 |
## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (pressure_self_cw... > 0 0.18 0.12 -0.03 0.37 12.61
## Post.Prob Star
## 1 0.93
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.
df_double$reactance_ordinal <- factor(df_double$reactance,
levels = 0:5,
ordered = TRUE)
formula <- bf(
reactance_ordinal ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
set_prior("normal(0, 2.5)", class = "b"),
set_prior("normal(0, 5)", class = "Intercept"),
set_prior("normal(0, 1)", class = "sd", group = "coupleID", lb = 0),
set_prior("normal(0, 0.075)", class = "ar", lb = -1, ub = 1),
set_prior("normal(0.5, 2.0)", class = "sderr", lb = 0)
)
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
reactance_ordinal <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::cumulative(),
control = list(adapt_delta = 0.95),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "reactance_ordinal")
)## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
##
## Computed from 12000 by 756 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -683.1 32.1
## p_loo 112.0 7.4
## looic 1366.2 64.2
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.1, 1.3]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 753 99.6% 143
## (0.7, 1] (bad) 3 0.4% <NA>
## (1, Inf) (very bad) 0 0.0% <NA>
## See help('pareto-k-diagnostic') for details.
summarize_brms(
reactance_ordinal,
model_rows_fixed = model_rows_fixed_ordinal,
model_rows_random = model_rows_random_ordinal,
model_rownames_fixed = model_rownames_fixed_ordinal,
model_rownames_random = model_rownames_random_ordinal,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack_ordinal)| OR | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| Intercepts | ||||||
| Intercept | NA | NA | NA | NA | NA | NA |
| Intercept[1] | 4.29* | 2.49 | 8.21 | 1.001 | 2256.01 | 1951.78 |
| Intercept[2] | 9.75* | 5.40 | 21.51 | 1.002 | 1435.82 | 1081.80 |
| Intercept[3] | 28.61* | 14.76 | 75.24 | 1.003 | 1210.60 | 778.30 |
| Intercept[4] | 133.42* | 58.72 | 466.56 | 1.004 | 1094.69 | 724.17 |
| Intercept[5] | 5469.93* | 1336.97 | 45257.45 | 1.003 | 1166.93 | 814.31 |
| Within-Person Effects | ||||||
| Daily persuasion experienced | 0.84* | 0.70 | 0.99 | 1.000 | 6298.44 | 5973.36 |
| Daily persuasion utilized (partner’s view) | 1.02 | 0.83 | 1.24 | 1.000 | 10312.32 | 8638.15 |
| Daily pressure experienced | 1.92* | 1.20 | 2.93 | 1.001 | 3247.00 | 2869.57 |
| Daily pressure utilized (partner’s view) | 1.25 | 0.74 | 2.11 | 1.000 | 8335.13 | 7024.63 |
| Daily pushing experienced | 1.17 | 0.96 | 1.46 | 1.000 | 7133.18 | 6701.14 |
| Daily pushing utilized (partner’s view) | 0.91 | 0.69 | 1.19 | 1.001 | 9296.13 | 7955.74 |
| Day | 1.48 | 0.72 | 3.06 | 1.000 | 11603.05 | 7629.89 |
| Daily weartime | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | ||||||
| Mean persuasion experienced | 1.11 | 0.38 | 3.29 | 1.001 | 5666.63 | 6811.76 |
| Mean persuasion utilized (partner’s view) | 1.40 | 0.45 | 4.79 | 1.001 | 5800.97 | 6413.49 |
| Mean pressure experienced | 3.71* | 1.20 | 12.26 | 1.001 | 5312.91 | 5602.60 |
| Mean pressure utilized (partner’s view) | 1.20 | 0.36 | 3.84 | 1.001 | 6106.53 | 7633.92 |
| Mean pushing experienced | 1.18 | 0.26 | 5.67 | 1.000 | 7661.62 | 8475.36 |
| Mean pushing utilized (partner’s view) | 0.09* | 0.01 | 0.57 | 1.001 | 5985.19 | 7322.95 |
| Mean weartime | NA | NA | NA | NA | NA | NA |
| Random Effects | ||||||
| sd(Intercept) | 0.84 | 0.48 | 1.31 | 1.00 | 2922.60 | 3336.30 |
| sd(Daily persuasion experienced) | 0.18 | 0.01 | 0.44 | 1.00 | 2882.76 | 5395.24 |
| sd(Daily persuasion utilized (partner’s view)) | 0.22 | 0.01 | 0.53 | 1.00 | 3661.25 | 5972.87 |
| sd(Daily pressure experienced) | 0.57 | 0.08 | 1.15 | 1.00 | 2764.88 | 2568.59 |
| sd(Daily pressure utilized (partner’s view)) | 0.45 | 0.02 | 1.34 | 1.00 | 4034.58 | 7034.00 |
| sd(Daily pushing experienced) | 0.22 | 0.01 | 0.52 | 1.00 | 3442.46 | 5129.78 |
| sd(Daily pushing utilized (partner’s view)) | 0.19 | 0.01 | 0.61 | 1.00 | 4713.67 | 6572.87 |
| Additional Parameters | ||||||
| ar[1] | 0.00 | -0.15 | 0.14 | 1.00 | 16982.67 | 8940.39 |
| nu | NA | NA | NA | NA | NA | NA |
| shape | NA | NA | NA | NA | NA | NA |
| sderr | 0.51 | 0.02 | 1.65 | 1.01 | 613.02 | 433.90 |
| sigma | NA | NA | NA | NA | NA | NA |
| disc | 1.00 | 1.00 | 1.00 | NA | NA | NA |
## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (pressure_self_cw... > 0 0.49 0.26 0.05 0.89 26.84
## Post.Prob Star
## 1 0.96 *
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.
introduce_binary_reactance <- function(data) {
data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
return(data)
}
df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
for (i in seq_along(implist)) {
implist[[i]] <- introduce_binary_reactance(implist[[i]])
}
}
formula <- bf(
is_reactance ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID),
autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b"),
brms::set_prior("normal(0, 10)", class = "Intercept", lb=0, ub=5), # range of the outcome scale
brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),
brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1)
#brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
is_reactance <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = bernoulli(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = 5000,
warmup = 2000,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", "is_reactance")
)## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
##
## Computed from 12000 by 756 log-likelihood matrix.
##
## Estimate SE
## elpd_loo -328.4 12.7
## p_loo 260.2 10.7
## looic 656.8 25.5
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.6, 1.2]).
##
## Pareto k diagnostic values:
## Count Pct. Min. ESS
## (-Inf, 0.7] (good) 35 4.6% 396
## (0.7, 1] (bad) 572 75.7% <NA>
## (1, Inf) (very bad) 149 19.7% <NA>
## See help('pareto-k-diagnostic') for details.
summarize_brms(
is_reactance,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) %>%
print_df(rows_to_pack = rows_to_pack)| OR | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|
| Intercept | 0.03* | 0.00 | 0.18 | 1.001 | 2961.20 | 5776.43 |
| Within-Person Effects | ||||||
| Daily persuasion experienced | 0.55 | 0.26 | 1.01 | 1.000 | 4130.22 | 6033.87 |
| Daily persuasion utilized (partner’s view) | 1.86 | 0.76 | 6.03 | 1.001 | 3737.98 | 5220.40 |
| Daily pressure experienced | 8.70* | 1.69 | 60.77 | 1.000 | 3710.48 | 6299.81 |
| Daily pressure utilized (partner’s view) | 2.19 | 0.33 | 18.42 | 1.000 | 6729.25 | 6651.90 |
| Daily pushing experienced | 2.34* | 1.07 | 6.63 | 1.000 | 3633.87 | 4827.24 |
| Daily pushing utilized (partner’s view) | 0.72 | 0.21 | 2.27 | 1.001 | 6444.05 | 6815.31 |
| Day | 3.78 | 0.38 | 43.96 | 1.000 | 6637.82 | 7554.77 |
| Daily weartime | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | ||||||
| Mean persuasion experienced | 13.42 | 0.90 | 291.21 | 1.000 | 3985.43 | 6196.30 |
| Mean persuasion utilized (partner’s view) | 5.05 | 0.31 | 91.34 | 1.000 | 7444.78 | 7562.53 |
| Mean pressure experienced | 132.06* | 3.62 | 5363.94 | 1.000 | 9338.38 | 8352.40 |
| Mean pressure utilized (partner’s view) | 6.03 | 0.15 | 270.01 | 1.000 | 8879.00 | 9243.83 |
| Mean pushing experienced | 4.47 | 0.10 | 220.19 | 1.000 | 7718.10 | 8220.27 |
| Mean pushing utilized (partner’s view) | 0.06 | 0.00 | 3.21 | 1.000 | 8490.07 | 8668.77 |
| Mean weartime | NA | NA | NA | NA | NA | NA |
| Random Effects | ||||||
| sd(Intercept) | 3.39 | 1.86 | 5.30 | 1.00 | 2132.54 | 3149.41 |
| sd(Daily persuasion experienced) | 0.58 | 0.03 | 1.50 | 1.00 | 1978.49 | 4478.41 |
| sd(Daily persuasion utilized (partner’s view)) | 1.50 | 0.42 | 2.95 | 1.00 | 2329.46 | 2696.99 |
| sd(Daily pressure experienced) | 1.97 | 0.11 | 4.47 | 1.00 | 1972.68 | 3600.22 |
| sd(Daily pressure utilized (partner’s view)) | 1.35 | 0.05 | 3.81 | 1.00 | 4817.26 | 5523.96 |
| sd(Daily pushing experienced) | 0.84 | 0.05 | 2.02 | 1.00 | 2576.31 | 3993.09 |
| sd(Daily pushing utilized (partner’s view)) | 0.75 | 0.03 | 2.23 | 1.00 | 4299.27 | 6263.29 |
| Additional Parameters | ||||||
| ar[1] | 0.06 | -0.16 | 0.27 | 1.00 | 1980.35 | 3211.48 |
| nu | NA | NA | NA | NA | NA | NA |
| shape | NA | NA | NA | NA | NA | NA |
| sderr | 5.90 | 3.10 | 9.31 | 1.00 | 1442.76 | 1679.03 |
| sigma | NA | NA | NA | NA | NA | NA |
## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (pressure_self_cw... > 0 1.31 0.99 -0.23 3.01 11.11
## Post.Prob Star
## 1 0.92
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.
if (report_ordinal) {
model_rows_random_final <- model_rows_random_ordinal
model_rows_fixed_final <- model_rows_fixed_ordinal
model_rownames_fixed_final <- model_rownames_fixed_ordinal
model_rownames_random_final <- model_rownames_random_ordinal
rows_to_pack_final <- rows_to_pack_ordinal
} else {
model_rows_random_final <- model_rows_random
model_rows_fixed_final <- model_rows_fixed
model_rownames_fixed_final <- model_rownames_fixed
model_rownames_random_final <- model_rownames_random
rows_to_pack_final <- rows_to_pack
}
all_models <- report_side_by_side(
pa_sub,
pa_obj_log,
mood,
reactance,
is_reactance,
model_rows_random = model_rows_random_final,
model_rows_fixed = model_rows_fixed_final,
model_rownames_random = model_rownames_random_final,
model_rownames_fixed = model_rownames_fixed_final
) ## [1] "pa_sub"
## [1] "pa_obj_log"
## [1] "mood"
## [1] "reactance"
## [1] "is_reactance"
# pretty printing
summary_all_models <- all_models %>%
print_df(rows_to_pack = rows_to_pack_final) %>%
add_header_above(
c(" ", "Subjective MVPA" = 2,
"Device-Based MVPA" = 2,
"Mood" = 2,
"Reactance Gaussian" = 2,
"Reactance Dichotome" = 2)
)
export_xlsx(
summary_all_models,
rows_to_pack = rows_to_pack_final,
file.path("Output", "AllModels.xlsx"),
merge_option = 'both',
simplify_2nd_row = TRUE,
colwidths = c(38, 7.2, 13.3, 7.2, 13.3,7.2, 13.3,7.2, 13.3,7.2, 13.3),
line_above_rows = c(1,2),
line_below_rows = c(-1)
)
summary_all_models| IRR pa_sub | 95% CI pa_sub | exp(Est.) pa_obj_log | 95% CI pa_obj_log | b mood | 95% CI mood | b reactance | 95% CI reactance | OR is_reactance | 95% CI is_reactance | |
|---|---|---|---|---|---|---|---|---|---|---|
| Intercept | 27.35* | [21.03, 35.95] | 118.09* | [106.05, 131.83] | 4.73* | [ 4.52, 4.94] | 0.51* | [ 0.33, 0.70] | 0.03* | [0.00, 0.18] |
| Within-Person Effects | ||||||||||
| Daily persuasion experienced | 1.20* | [ 1.07, 1.35] | 1.03 | [ 1.00, 1.06] | 0.00 | [-0.03, 0.04] | -0.05 | [-0.11, 0.01] | 0.55 | [0.26, 1.01] |
| Daily persuasion utilized (partner’s view) | 1.19* | [ 1.06, 1.34] | 1.02 | [ 0.99, 1.05] | 0.02 | [-0.02, 0.05] | 0.00 | [-0.06, 0.07] | 1.86 | [0.76, 6.03] |
| Daily pressure experienced | 0.94 | [ 0.71, 1.30] | 0.95 | [ 0.89, 1.01] | -0.05 | [-0.16, 0.05] | 0.26* | [ 0.04, 0.47] | 8.70* | [1.69, 60.77] |
| Daily pressure utilized (partner’s view) | 1.16 | [ 0.88, 1.59] | 0.98 | [ 0.92, 1.05] | -0.04 | [-0.18, 0.09] | 0.14 | [-0.06, 0.40] | 2.19 | [0.33, 18.42] |
| Daily pushing experienced | 1.13 | [ 0.92, 1.41] | 1.03 | [ 0.98, 1.07] | 0.02 | [-0.04, 0.09] | 0.08* | [ 0.00, 0.17] | 2.34* | [1.07, 6.63] |
| Daily pushing utilized (partner’s view) | 1.13 | [ 0.95, 1.36] | 1.03 | [ 0.99, 1.07] | 0.06* | [ 0.00, 0.12] | -0.02 | [-0.10, 0.07] | 0.72 | [0.21, 2.27] |
| Day | 0.79 | [ 0.58, 1.10] | 0.96 | [ 0.88, 1.05] | 0.22* | [ 0.06, 0.38] | 0.10 | [-0.17, 0.35] | 3.78 | [0.38, 43.96] |
| Daily weartime | NA | NA | 1.00* | [ 1.00, 1.00] | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | ||||||||||
| Mean persuasion experienced | 1.61 | [ 0.83, 3.13] | 1.09 | [ 0.81, 1.45] | 0.32 | [-0.26, 0.88] | 0.08 | [-0.29, 0.45] | 13.42 | [0.90, 291.21] |
| Mean persuasion utilized (partner’s view) | 1.19 | [ 0.62, 2.28] | 0.96 | [ 0.72, 1.29] | 0.23 | [-0.36, 0.80] | 0.14 | [-0.25, 0.55] | 5.05 | [0.31, 91.34] |
| Mean pressure experienced | 0.50 | [ 0.22, 1.11] | 0.97 | [ 0.71, 1.34] | -0.29 | [-0.89, 0.30] | 0.62* | [ 0.21, 1.03] | 132.06* | [3.62, 5363.94] |
| Mean pressure utilized (partner’s view) | 0.43* | [ 0.19, 0.96] | 0.98 | [ 0.72, 1.32] | -0.31 | [-0.89, 0.28] | 0.16 | [-0.28, 0.58] | 6.03 | [0.15, 270.01] |
| Mean pushing experienced | 1.71 | [ 0.64, 4.56] | 1.02 | [ 0.66, 1.56] | 0.25 | [-0.55, 1.04] | -0.28 | [-0.82, 0.27] | 4.47 | [0.10, 220.19] |
| Mean pushing utilized (partner’s view) | 2.03 | [ 0.76, 5.67] | 1.29 | [ 0.85, 1.96] | 0.39 | [-0.41, 1.18] | -0.59* | [-1.16, -0.01] | 0.06 | [0.00, 3.21] |
| Mean weartime | NA | NA | 1.00 | [ 1.00, 1.00] | NA | NA | NA | NA | NA | NA |
| Random Effects | ||||||||||
| sd(Intercept) | 0.61 | [ 0.45, 0.83] | 0.30 | [0.23, 0.39] | 0.59 | [0.46, 0.77] | 0.27 | [ 0.15, 0.41] | 3.39 | [ 1.86, 5.30] |
| sd(Daily persuasion experienced) | 0.09 | [ 0.00, 0.24] | 0.05 | [0.03, 0.08] | 0.03 | [0.00, 0.07] | 0.05 | [ 0.00, 0.13] | 0.58 | [ 0.03, 1.50] |
| sd(Daily persuasion utilized (partner’s view)) | 0.08 | [ 0.00, 0.21] | 0.05 | [0.02, 0.08] | 0.06 | [0.01, 0.11] | 0.04 | [ 0.00, 0.13] | 1.50 | [ 0.42, 2.95] |
| sd(Daily pressure experienced) | 0.17 | [ 0.01, 0.53] | 0.05 | [0.00, 0.14] | 0.11 | [0.01, 0.28] | 0.39 | [ 0.22, 0.62] | 1.97 | [ 0.11, 4.47] |
| sd(Daily pressure utilized (partner’s view)) | 0.16 | [ 0.01, 0.49] | 0.04 | [0.00, 0.11] | 0.19 | [0.03, 0.37] | 0.25 | [ 0.01, 0.63] | 1.35 | [ 0.05, 3.81] |
| sd(Daily pushing experienced) | 0.28 | [ 0.02, 0.58] | 0.06 | [0.00, 0.14] | 0.09 | [0.02, 0.17] | 0.10 | [ 0.01, 0.23] | 0.84 | [ 0.05, 2.02] |
| sd(Daily pushing utilized (partner’s view)) | 0.12 | [ 0.00, 0.32] | 0.03 | [0.00, 0.08] | 0.05 | [0.00, 0.13] | 0.05 | [ 0.00, 0.15] | 0.75 | [ 0.03, 2.23] |
| Additional Parameters | ||||||||||
| ar[1] | 0.03 | [-0.94, 0.94] | 0.29 | [0.26, 0.33] | 0.45 | [0.42, 0.48] | 0.01 | [-0.08, 0.09] | 0.06 | [-0.16, 0.27] |
| nu | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| shape | 0.14 | [ 0.13, 0.14] | NA | NA | NA | NA | NA | NA | NA | NA |
| sderr | 0.05 | [ 0.00, 0.13] | NA | NA | NA | NA | NA | NA | 5.90 | [ 3.10, 9.31] |
| sigma | NA | NA | 0.55 | [0.54, 0.57] | 0.87 | [0.85, 0.89] | 0.93 | [ 0.88, 0.98] | NA | NA |
Analyses were conducted using the R Statistical language (version 4.4.1; R Core Team, 2024) on Windows 11 x64 (build 22635)